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Reversed CF: A fast collaborative filtering algorithm using a k-nearest neighbor graph

Cited 51 time in Web of Science Cited 75 time in Scopus
Authors
Park, Youngki; Park, Sungchan; Jung, Woosung; Lee, Sang-goo
Issue Date
2015
Publisher
Elsevier
Citation
Expert Systems with Applications, vol.42, pp. 4022-4028
Keywords
Reversed CFCollaborative filteringk-Nearest neighbor graphGreedy filtering
Abstract
User-based and item-based collaborative filtering (CF) methods are two of the most widely used techniques in recommender systems. While these algorithms are widely used in both industry and academia owing to their simplicity and acceptable level of accuracy, they require a considerable amount of time in finding top-k similar neighbors (items or users) to predict user preferences of unrated items. In this paper, we present Reversed CF (RCF), a rapid CF algorithm which utilizes a k-nearest neighbor (k-NN) graph. One main idea of this approach is to reverse the process of finding k neighbors; instead of finding k similar neighbors of unrated items, RCF finds the k-nearest neighbors of rated items. Not only does this algorithm perform fewer predictions while filtering out inaccurate results, but it also enables the use of fast k-NN graph construction algorithms. The experimental results show that our approach outperforms traditional user-based/item-based CF algorithms in terms of both preprocessing time and query processing time without sacrificing the level of accuracy.
Language
English
URI
https://hdl.handle.net/10371/95428
DOI
https://doi.org/10.1016/j.eswa.2015.01.001
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Journal Papers (저널논문_컴퓨터공학부)
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